1. Michael M. Wagner, MD PhD Professor, Department of Biomedical Informatics, University of Pittsburgh School of Medicine

2. © 2011 University of Pittsburgh
Probabilistic Disease Surveillance and Control: influenza Monitoring System, Allegheny County, PA 2009 Influenza H1N1
daily fractions of ED patients with +flu test, influenza-like illness, and fever
October 26
September 8th
This slide shows the fraction of ED patients per day with evidence of influenza like illness in Allegheny County. The lowest line is the fraction of patients with positive laboratory tests for influenza (either A or B); red line are patients that satisfy the CDC ILI case definition ( T>100.4 AND one respiratory symptom) as determined by NLP of dictated ED reports; green is the sum of posterior probabilities of ILI of all patients seen in ED on that day, as determined by NLP and a Bayesian diagnostic network; and blue is fraction of patients with T>100.4
The greyed area delineates the time period up to Sept 8, 2009 from the period after. The following slide shows the prediction of an algorithm using only the data in the green line.
June 1
© 2011 University of Pittsburgh
December 31

3. © 2011 University of Pittsburgh
Predictions of Outbreak Characterization and Detection System on Sept 8
The top curve is the incidence curve generated by the most likely of 50,000 possible SEIR models. The probability of this epidemic model given the surveillance data represented in the green line on the previous slide up to Sept 8th, is The second most probable epidemic model for Allegheny County, given the data available up until Sept 8th has a probability of The peak date of the most likely SEIR epidemic models and the area under the curves (total number infected) match closely with what happened in Allegheny County (see first slide for peak date, total number infected of around 20% was estimated by Ted Ross in a published paper).
The histograms on the right summarize the 50,000 SEIR models. The first histogram, “total infected” shows the area under the epidemic curves, which is bimodal around 400 and The second histogram shows the peak data, which is predicted to be between 15 and 130 days from Sept. 8th. The fourth through sixth histograms are key outbreak characteristics: Ro, the reproductive rate; latent period, and infectious period.
© 2011 University of Pittsburgh